Building predictive agents (RFM + MCP) (kumo.ai)

KumoRFM introduces a groundbreaking approach to transform reactive AI agents into predictive ones by leveraging relational data without the need for traditional model training or feature engineering. Using Graph Transformer architectures, KumoRFM interprets multi-table enterprise databases as temporal, heterogeneous graphs, enabling a single foundation model to generate diverse business predictions—like customer churn, inventory demand, and equipment maintenance—directly from existing schemas. This eliminates months of iterative model development, significantly lowering barriers for deploying predictive intelligence at scale. The KumoRFM MCP server implements the Model Context Protocol (MCP), an open standard that seamlessly connects AI agents—such as LangChain, OpenAI Agents SDK, and others—with KumoRFM’s predictive capabilities via structured tool interfaces. Agents can discover data, manage graph representations, and execute predictive queries in an integrated, multi-step reasoning workflow. This enables enterprises to build smarter agents that proactively forecast outcomes, optimize operations, and personalize recommendations based on live data, all without custom ML pipelines or ongoing maintenance overhead. By bridging the gap between relational data and foundation model intelligence, KumoRFM promises to accelerate the adoption of predictive AI in business ecosystems. Its zero-training paradigm and broad compatibility with existing agent frameworks empower developers to rapidly prototype and deploy predictive applications, marking a significant leap forward in enterprise AI automation and decision-making.